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The aim of the supplier selection process is to reduce purchasing risks, boost customer profitability, and cultivate enduring, close relationships between suppliers and buyers. This process stands as a pivotal component of supply chain management (SCM), making supplier selection a critical concern for establishing an effective supply chain system. In this instance, a case study has been presented for supplier selection employing Analytic Hierarchy Process (AHP) in conjunction with the Multi-Criteria Decision Making (MCDM) technique. We have explained it using fuzzy data. It not only contributed on utility of fuzzy AHP methodology, but also provided comprehensive literature review of MCDM problems. Furthermore, by outlining the steps of fuzzy AHP in a straightforward numerical manner, this study can serve as a guide for implementing the methodology in other (MCDM) problems. In this study, we evaluate four suppliers using fuzzy AHP. This method can be used when one has to select one particular supplier from a number of suppliers in a short time span. It will be also helpful to the Pharmacists to select the best supplier who can fulfil all their needs. Despite the abundance of studies on supplier selection, there has been relatively less investigation into the evaluation and selection of suppliers using specific measures tailored to the healthcare sector. To bridge this gap, this research puts forth a guideline for selecting suppliers of baby hygiene products. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of studying digitization transformation of the supply chain is to understand how digital technologies and processes are changing the way supply chains operate and to identify the opportunities and challenges associated with this transformation. Studying digitization transformation of the supply chain is important because it can help global businesses in identifying the best practices in supply chain management (SCM) systems and enhance supply chain performance. Hence, this research study is contributing in revealing the outcomes of digital inclusiveness in overall SCM for the growth of retail and e-commerce based platforms. Design/methodology/approach: This research is using both descriptive and explanatory research designs to provide a comprehensive understanding of the problems in SCM. Descriptive research provides a detailed description of the characteristics of the population under study, while explanatory research identifies the causal relationships between the variables. Descriptive research has helped us to develop hypotheses about the relationships between variables that can be tested using explanatory research. Explanatory research has been used to validate the findings of descriptive research. By using both descriptive and explanatory research designs, our research design has increased the generalizability of our findings. Findings: According to this study, businesses intend to change their supply chain strategies after the wake of competitive era to make them more robust, sustainable and collaborative with suppliers, customers and stakeholders by investing more in SCM technology like Blockchain, AI, analytics, robotic process automation and data control centers. This study evaluates the impact of digitization on supply chain systems. This includes assessing the benefits of digitization and identifying the factors that contribute to successful implementation. This research is studying the role of data analytics in SCM and how it can be leveraged to improve efficiency, reduce costs and increase transparency. Research limitations/implications: The study highlights the importance of adopting digitization in supply chain systems to improve supply chain robustness, sustainability and collaboration with stakeholders. This study's emphasis on data analytics in SCM presents an opportunity for businesses to gain insights into their supply chain systems and make data-driven decisions. This can enhance efficiency, reduce costs and improve overall supply chain performance. The study's focus on SCM technology and data analytics may overlook other factors that contribute to successful SCM, such as organizational culture, human resources and supply chain governance. Originality/value: This study will complement to the existing body of information, management theory and practice and will benefit all. The research work is original and can be implemented worldwide to promote digitization in SCM for smooth transactions in the entire chain of wholesalers, retail distributors and customers. [ABSTRACT FROM AUTHOR]

Abstract Strategic supply chain management (SCM) is essential for organizations striving to optimize performance and attain their goals. Prediction of supply chain management distribution cost (SCMDC) is one branch of SCM and it’s essential for organizations striving to optimize performance and attain their goals. For this purpose, four machine learning algorithms, including random forest (RF), support vector machine (SVM), multilayer perceptron (MLP) and decision tree (DT), along with deep learning using convolutional neural network (CNN), was used to predict and analyze SCMDC. A comprehensive dataset consisting of 180,519 open-source data points was used for analyze and make the structure of each algorithm. Evaluation based on Root Mean Square Error (RMSE) and Correlation coefficient (R2) show the CNN model has high accuracy in SCMDC prediction than other models. The CNN algorithm demonstrated exceptional accuracy on the test dataset, with an RMSE of RMSE of 0.528 and an R2 value of 0.953. Notable advantages of CNNs include automatic learning of hierarchical features, proficiency in capturing spatial and temporal patterns, computational efficiency, robustness to data variations, minimal preprocessing requirements, end-to-end training capability, scalability, and widespread adoption supported by extensive research. These attributes position the CNN algorithm as the preferred choice for precise and reliable SCMDC predictions, especially in scenarios requiring rapid responses and limited computational resources.

The integration of Artificial Intelligence (AI) within supply chain management (SCM) has precipitated a significant transformation in business processes, particularly in emerging economies like South Africa. This systematic literature review explores the extent of AI’s influence on SCM practices across various industries within the South African context, underpinning the potential of AI to improve operational efficiency, strategic decision-making, and innovation. This review thoroughly followed the PRISMA guidelines. The findings reveal that AI significantly contributes to the advancement of SCM by improving accuracy in demand forecasting, optimising resource allocation, and facilitating real-time decision-making. This review synthesizes current research, offering a comprehensive overview of AI’s transformative potential in SCM within South Africa. It also suggests areas for future research, particularly in addressing the challenges of AI implementation and exploring its impact on sustainable SCM practices. The implications for both practitioners and policymakers include prioritising digital infrastructure development, ethical AI integration, and encouraging public-private partnerships to support AI-driven innovations in supply chain networks. Future research should prioritise the development of practical methods that specifically target the demands and obstacles of incorporating AI into supply chains in South Africa, with the aim of promoting fair and sustainable growth.

Strategic supply chain management (SCM) is essential for organizations striving to optimize performance and attain their goals. Prediction of supply chain management distribution cost (SCMDC) is one branch of SCM and it’s essential for organizations striving to optimize performance and attain their goals. For this purpose, four machine learning algorithms, including random forest (RF), support vector machine (SVM), multilayer perceptron (MLP) and decision tree (DT), along with deep learning using convolutional neural network (CNN), was used to predict and analyze SCMDC. A comprehensive dataset consisting of 180,519 open-source data points was used for analyze and make the structure of each algorithm. Evaluation based on Root Mean Square Error (RMSE) and Correlation coefficient (R2) show the CNN model has high accuracy in SCMDC prediction than other models. The CNN algorithm demonstrated exceptional accuracy on the test dataset, with an RMSE of RMSE of 0.528 and an R2 value of 0.953. Notable advantages of CNNs include automatic learning of hierarchical features, proficiency in capturing spatial and temporal patterns, computational efficiency, robustness to data variations, minimal preprocessing requirements, end-to-end training capability, scalability, and widespread adoption supported by extensive research. These attributes position the CNN algorithm as the preferred choice for precise and reliable SCMDC predictions, especially in scenarios requiring rapid responses and limited computational resources. [ABSTRACT FROM AUTHOR]

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The integration of Artificial Intelligence (AI) within supply chain management (SCM) has precipitated a significant transformation in business processes, particularly in emerging economies like South Africa. This systematic literature review explores the extent of AI's influence on SCM practices across various industries within the South African context, underpinning the potential of AI to improve operational efficiency, strategic decision-making, and innovation. This review thoroughly followed the PRISMA guidelines. The findings reveal that AI significantly contributes to the advancement of SCM by improving accuracy in demand forecasting, optimising resource allocation, and facilitating real-time decision-making. This review synthesizes current research, offering a comprehensive overview of AI's transformative potential in SCM within South Africa. It also suggests areas for future research, particularly in addressing the challenges of AI implementation and exploring its impact on sustainable SCM practices. The implications for both practitioners and policymakers include prioritising digital infrastructure development, ethical AI integration, and encouraging public-private partnerships to support AI-driven innovations in supply chain networks. Future research should prioritise the development of practical methods that specifically target the demands and obstacles of incorporating AI into supply chains in South Africa, with the aim of promoting fair and sustainable growth. [ABSTRACT FROM AUTHOR]

The United Nations Convention on Climate Change 2015 mandates reducing the carbon footprint to reduce global warming. Considering environmental concerns, electric vehicles (EVs) spearhead the move towards green mobility. Niti Aayog (Indian Government) has envisaged an "EV only" scenario by 2030. Two wheelers, with almost 80% of the market share of the Indian automobile industry, will provide the primary thrust for EVs. The holistic sustainability of the Indian EV two-wheeler industry will depend upon its vendor partnerships and processes, which are examined in this paper through a systematic review of the literature available from all known sources. This study, after reviewing over 165 papers apart from government and independent reports, also explores how sustainability and allied topics like green supply chain management in business decision making promotes efficiency, controls expenditure, enhances customer delight, increases sales and market share, optimizes risk management strategies and promotes profitability. Considering the restricted availability of the literature on the Indian automobile industry in general, and specifically on the Indian EV or EV two-wheeler industry, this work will help in bringing focus on this area of fast-burgeoning importance and will pave the way for the establishment of a conceptual framework for research. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to formulate a new class of vehicle routing problem with an objective to minimise the total cost of raw material collection and derive a new approach to solve optimization problems. This study can help to select the optimum number of suppliers based on cost. Design/methodology/approach: To model the raw material vehicle routing problem, a mixed integer linear programming (MILP) problem is formulated. An interesting phenomenon added to the proposed problem is that there is no compulsion to visit all suppliers. To guarantee the demand of semiconductor industry, all visited suppliers should reach a given raw material capacity requirement. To solve the proposed model, the authors developed a novel hybrid approach that is a combination of block and edge recombination approaches. To avoid bias, the authors compare the results of the proposed methodology with other known approaches, such as genetic algorithms (GAs) and ant colony optimisation (ACO). Findings: The findings indicate that the proposed model can be useful in industries, where multiple suppliers are used. The proposed hybrid approach provides a better sequence of suppliers compared to other heuristic techniques. Research limitations/implications: The data used in the proposed model is generated based on previous literature. The problem derives from the assumption that semiconductor industries use a variety of raw materials. Practical implications: This study provides a new model and approach that can help practitioners and policymakers select suppliers based on their logistics costs. Originality/value: This study provides two important contributions in the context of the supply chain. First, it provides a new variant of the vehicle routing problem in consideration of raw material collection; and second, it provides a new approach to solving optimisation problems. [ABSTRACT FROM AUTHOR]

In today's dynamic business environment, the accurate prediction of sales orders plays a critical role in optimizing Supply Chain Management (SCM) and enhancing operational efficiency. In a rapidly changing, Fast-Moving Consumer Goods (FMCG) business, it is essential to analyze the sales of the products and accordingly plan the supply. Due to low data volume and complexity, traditional forecasting methods struggle to capture intricate patterns. Domain Adversarial Neural Networks (DANNs) offer a promising solution by integrating transfer learning techniques to improve prediction accuracy across diverse datasets. This study presents a new sales order prediction framework that combines DANN-based feature extraction and various machine learning models. The DANN method generalizes the data, maintaining the data behavior's originality. The approach addresses challenges like limited data availability and high variability in sales behavior. Using the transfer learning approach, the DANN model is trained on the training data, and this pre-trained DANN model extracts relevant features from unknown products. In contrast, Machine Learning (ML) algorithms are used to build predictive models based on it. The hyperparameter tuning of ensemble models such as Decision Tree (DT) and Random Forest (RF) is also performed. Models like the DT and RF Regressor perform better than Linear Regression and Support Vector Regressor. Notably, even without hyperparameter tuning, the Extreme Gradient Boost (XGBoost) Regressor model outperforms all the other models. This comprehensive analysis highlights the comparative benefits of various models and establishes the superiority of XGBoost in predicting sales orders effectively. [ABSTRACT FROM AUTHOR]

Abstract Purpose This research aims to study the impact of sustainable supply chain management on both competitive advantage and organizational performance in the Ethiopian manufacturing industry. Design/methodology/approach The objectives are achieved through collecting and analyzing data from 221 Ethiopian manufacturing industries. This research employs a quantitative approach, specifically descriptive and causal research methods. The data are collected by questionnaires administered directly to a sample of 221 respondents who are managers and supervisors in the manufacturing industry. In addition, data analysis was performed using structural equation modeling in the Smart-PLS Software version (SmartPLS 4.0). Findings The research reveals that SSCM substantially and positively impacts competitive advantage and organizational performance. Furthermore, statistical findings prove the connection between competitive advantage and organizational performance. Moreover, competitive advantage indirectly influences the relationship between SSCM and OP. The results suggest that successfully implementing SSCM can improve competitive advantage and OP. Originality/value Considering the triple-bottom-line approach and the mediating effects of competitive advantage, this study is the first to analyze the relationship between SSCM and manufacturing performance in Ethiopia. This study adds to the existing literature by providing empirical evidence on the impact of sustainable supply chain management (SSCM) on competitive advantage and organizational performance in the manufacturing industry of emerging markets. Research limitations/implications The research is based on a cross-sectional study, which may prevent the generalization of findings derived from the current study. The analyzed variable in this study quantified OP, which is widely recognized as a very dynamic concept.

In light of dynamic market conditions, exacerbated by the global COVID-19 pandemic, there is a pressing need for businesses to enhance their Supply Chain Management (SCM) strategies. The surge in e-Commerce, technological evolution, disruptions in supply chains, and the imperative for collaborative risk management underscore the critical necessity for innovative and resilient SCM practices, forming the basis for this study. This study explores the integrated concepts of supply chain management (SCM) and logistics, focusing on their symbiotic relationship and impact on logistics performance. Emphasizing the significance of efficient logistics in enhancing customer value and cost-effectiveness, the research investigates the potential benefits derived from lean and agile logistics systems. The study employs a comprehensive approach, integrating lean logistics to optimize material flow and reduce waste and costs, while agile logistics enhances flexibility and responsiveness to dynamic demands. The goal is to develop a robust framework for designing a lean-agile supply chain, ultimately aiming to improve supply chain partnerships and overall logistics performance. The findings of this research contribute valuable insights to logistics companies, guiding them toward more effective supply network management. By understanding and leveraging the synergies between lean and agile logistics within the SCM landscape, organizations can enhance their logistics performance and adapt to the evolving demands of the market. This research adds to the existing body of knowledge by elucidating the integrated dynamics of SCM and logistics, specifically emphasizing the coalescence of lean and agile logistics systems. The proposed framework provides a practical guide for logistics companies seeking to optimize their supply networks and improve overall logistics performance. [ABSTRACT FROM AUTHOR]

Web service composition is crucial for creating valuable services by integrating pre-existing ones. With their service-oriented architecture (SOA), which can be used for any system design, web services can increase flexibility. Fusing Web services architecture with Semantic Web services can better assist supply chain coordination in a distributive, autonomous, and ever-changing corporate environment than current information technology. Decisions must be made quickly and with enough information many systems fail to provide real-time supply chain insight. Forecasting, inventory management, and decision-making may all be impacted by poor data quality. Modifying preexisting systems according to unique organizational needs may be challenging and expensive. Hence, this paper proposes a semantic web service-based supply chain management framework (SWS-SCMF) to analyze the web services in supply chains and examine how they interact using Web Ontology Language (OWL)-S, including automated discovery, construction, and invocation. The suggested method for improving supply chain data integration uses an ontology-based multiple-agent decision support system. Different accessibility tools, data formats, management information systems, semantic web, and databases are integrated across the five interconnected levels of the system. Businesses may find the proposed approach useful for data and information sharing when dealing with complex supply chain management. The suggested SWS-SCMF is an adaptable, accurate, and effective method for bidirectional chaining composition that uses mediators to enable the automated composition of Semantic Web services. The numerical results show that our proposed method enhances the overall performance ratio by 94%, accuracy ratio by 98%, and supply chain management ratio by 91% compared to other methods. [ABSTRACT FROM AUTHOR]

Purpose: This research aims to study the impact of sustainable supply chain management on both competitive advantage and organizational performance in the Ethiopian manufacturing industry. Design/methodology/approach: The objectives are achieved through collecting and analyzing data from 221 Ethiopian manufacturing industries. This research employs a quantitative approach, specifically descriptive and causal research methods. The data are collected by questionnaires administered directly to a sample of 221 respondents who are managers and supervisors in the manufacturing industry. In addition, data analysis was performed using structural equation modeling in the Smart-PLS Software version (SmartPLS 4.0). Findings: The research reveals that SSCM substantially and positively impacts competitive advantage and organizational performance. Furthermore, statistical findings prove the connection between competitive advantage and organizational performance. Moreover, competitive advantage indirectly influences the relationship between SSCM and OP. The results suggest that successfully implementing SSCM can improve competitive advantage and OP. Originality/value: Considering the triple-bottom-line approach and the mediating effects of competitive advantage, this study is the first to analyze the relationship between SSCM and manufacturing performance in Ethiopia. This study adds to the existing literature by providing empirical evidence on the impact of sustainable supply chain management (SSCM) on competitive advantage and organizational performance in the manufacturing industry of emerging markets. Research limitations/implications: The research is based on a cross-sectional study, which may prevent the generalization of findings derived from the current study. The analyzed variable in this study quantified OP, which is widely recognized as a very dynamic concept. [ABSTRACT FROM AUTHOR]

With the rapid advancements in Information and Communication Technologies (ICT) and the widespread enthusiasm of both theoreticians and practitioners, the broader transition to Industry 4.0 (I4.0) in major industries appears imminent. This empirical study analyzes business data from 1140 automotive companies operating in Europe, utilizing various business intelligence platforms and employing decision tree analytics to establish connections between enablers, drivers, company size, and financial resources. The goal is to identify persistent barriers hindering the rational transition to Industry 4.0. The findings reveal an uneven transformation within the industry nexus. While larger companies possess the financial means to allocate collective intelligence, technical resources, and drive necessary for fulfilling I4.0 requirements, smaller members of the nexus lag behind despite their enthusiasm and intent. This imbalanced evolution poses a threat to the comprehensive transformation required for realizing all the benefits of Industry 4.0 within the sector. The primary discovery indicates that small to medium-sized enterprises do not exhibit the same rates of Industry 4.0 adoption, a lag highly correlated with their available financial and human resources for digital transition. The decision tree proposed in this study offers guidelines for achieving an Industry 4.0-compliant nexus. Given its diversity and substantial global impact, the case study from the automotive industry proves intriguing and may later be generalized to other sectors. The study's outcome could empower engineering managers and researchers to implement, execute, and assess the impact of digital strategies based on the financial capabilities of industrial institutions. [ABSTRACT FROM AUTHOR]

Background: In the fourth-year period (2018–2019 to 2021–2022) of the Public Finance Management Act (PFMA) audit, the Auditor-General South Africa (AGSA) reported that the Department of Water and Sanitation (DWS) had incurred approximately R1.7 billion in irregular expenditure; patterns of such expenditure were identified. The AGSA indicated this as a clear non-compliance with the Supply Chain Management (SCM) legislation. Aim: This article evaluated the legislative frameworks to inhibit irregular expenditure and proposed a framework for its management. Setting: The study was conducted in the DWS in South Africa. Methods: The researchers referred to available documentation and adopted a qualitative research approach. Structured interviews were conducted with 10 out of 26 possible participants from the organisational structure of National Treasury, DWS SCM, Financial Management, Internal Audit, Risk Management, and Internal Controls with more than 10 years’ experience in SCM processes, PFMA (1999), Treasury Regulations (2005), Preferential Procurement Policy Framework Act (2000), Public Service Commission (1997), and the manifestation of public decision-making. Results: The ineffectiveness of implemented measures and a lack of consequence to hold officials responsible for transgressions were the primary causes of irregular expenditure. Conclusion: Based on these findings, recommendations aimed to strengthen the procurement process. This includes development and implementation of a standard operating procedure (SOP) manual, and implementing consequences for transgressions. A framework will help manage irregular expenditure and to identify corrective measures. Although the research was limited to the DWS, the results and recommendations are transferable to other departments with comparable challenges. Contribution: The study could help the DWS and other government departments or spheres with similar challenges in managing and reducing irregular expenditure.

Background: Although Blockchain Technology (BT) is one of the innovations that has considerable potential to improve business processes and enable new services for potential users, its implementation in supply chain management (SCM) of the automotive industry is only at its beginnings. From the growing number of publications focused on this issue, it is evident that the application of BT would significantly contribute to the development of the automotive industry and improve the supply chain of automotive components. Purpose: In this regard, the paper aims to analyze the challenges to the implementation of BT in SCM in the automotive industry sector through a systematic review of the literature and precise definition of the advantages and limitations that appear in supply chains after the application of BT. Study design/methodology/approach: The research is based on the application of systematic literature review methods. The paper presents the results and conclusions of 21 studies based on the search criteria outlined by the Web of Science, Scopus, and SpringerLink index databases. Findings/conclusions: The results suggest that insufficiently developed technology, lack of clear guidelines for implementation, incomplete standardization, legislative ambiguity, conflicts and insufficient cooperation between chain members appear as the biggest challenges for BT implementation. On the other hand, BT has great potential in reducing costs, providing higher quality products and services, and improving chain visibility in the automotive industry. Limitations/future research: The analysis of the papers in the above mentioned databases exclusively in English and the absence of empirical research stand out as the most prominent shortcomings. However, the obtained results of this study represent a quality basis for future research, which, judging by the popularity of the issue, will increase in frequency.

Managing various aspects of the Supply Chain (SC) has become increasingly challenging in today’s complex business landscape. To improve profitability, boost sales, and enhance customer satisfaction, it is crucial to explore future possibilities by adjusting key relational parameters. However, traditional forecasting methods often fail to provide accurate insights and are time-consuming. These limitations can be overcome using Artificial Intelligence (AI) algorithms such as Machine Learning (ML) and Deep Learning (DL). CatBoost algorithm is an ensemble-based ML model that can handle categorical variables effectively in its architecture, whereas other ML and DL models fail and require explicit encoding techniques. In this study, a predictive modeling approach using CatBoost to optimize supply chain processes using a mathematical approach was proposed. CatBoost evaluates the model on an e-commerce dataset through empirical analysis by tuning various hyperparameters to enhance prediction efficiency. A computational time limit of ten minutes was used to ensure practicality. Using regression and classification frameworks, this approach involves predicting sales, profit, and delivery times, and identifying potential customers. Consequently, analyzing the behavior of the learning rate and its impact on the performance metrics indicated that increasing the learning rate can lead to improved model performance.

Information system (IS) is the advent of technology which has helped many sectors of business in earning greater revenue and becoming sustainable in the long-term approach. Information systems are also strengthening the supply chain management (SCM) and the global context for better delivery in meeting consumer demands and generating profits. The study mainly highlights the internet applications which are applicable within the supply chain and the information sharing as well as communicational improvement which has been implemented to the SCM. Additionally, the information system has also enhanced the consumer service, reduction of cost during procurement, and accessibility to worldwide market in the supply chain. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak in 2020–2021 caused unprecedented disruptions to global supply networks. Companies worldwide faced significant challenges as they dealt with the unexpected surge in demand for specific goods and services. This study delves into the importance of supply chain coordination (SCCO), supply chain resilience (SCRE), and supply chain robustness (SCRB), considering supply chain flexibility (SCFL) and Internet of Things and Big Data Analytics (IoT-BDA) integration. We explore how SCFL influences SCCO, SCRE, and SCRB, enhancing supply chain performance (SCFP). Using a cross sectional approach, we collected survey-based responses to ensure comprehensive representation from the supply chain domain. A total of 217 complete responses were collected and analyzed using AMOS 20. The findings suggest that SCCO, SCRE, and SCRB act as mediators between SCFL and IoT-BDA. However, statistical significance between SCCO and SCRB with SCRE was not established. The study emphasizes the robust predictive nature of SCFL, highlighting its pivotal role in fostering SCCO, SCRE, and SCBR through empirical evidence. Furthermore, it emphasizes the influence of SCFL on enhancing SCFP, particularly in the post-pandemic era. [ABSTRACT FROM AUTHOR]

Abstract Companies must be able to identify their suppliers appropriately and effectively in order to survive in the competitive market conditions. In order to fulfill and surpass the expectations of the consumers and clients, companies need to interact with the relevant suppliers. It is a tough manner for companies to select the best supplier from a large number of relevant alternatives. The selection process of the appropriate supplier involves multiple interacting and competing factors. Generally, the selection process and its results cause a waste of time and money. For this purpose, MCDM methodologies are utilized to manage this complex process efficiently. MCDMs allows for consistent and accurate decision-making as well as the selection of the most appropriate supplier. MCDM is one the most preferred tool to select the best alternative under the conflicting and competitive criteria when the evaluations are made in crisp numbers. Therefore, MCDM methods are preferred in various applications in academia and real life. However, the evaluations could not be always possible with crisp numbers, especially in vague environments or evaluations needs qualitative data. This study is one of the first to combine the AHP and COPRAS supplier selection methods with interval-valued Pythagorean fuzzy (IPF) logic. The effectiveness of these IPF-AHP and IPF-COPRAS evaluations for the supplier selection problem is compared and examined. The experimental results of case scenarios show that IPF is an effective way to apply in decision-making applications. In addition, sensitivity analysis is conducted to evaluate the proposed methodologies. According to sensitivity analysis, the IPF-AHP and IPF-COPRAS be able to illustrate the effects of small changings in criteria weights. Therefore, companies can use the IPF-AHP and IPF-COPRAS to assist their decision-makers in identifying and selecting the best suppliers.

In today’s dynamic business environment, the accurate prediction of sales orders plays a critical role in optimizing Supply Chain Management (SCM) and enhancing operational efficiency. In a rapidly changing, Fast-Moving Consumer Goods (FMCG) business, it is essential to analyze the sales of the products and accordingly plan the supply. Due to low data volume and complexity, traditional forecasting methods struggle to capture intricate patterns. Domain Adversarial Neural Networks (DANNs) offer a promising solution by integrating transfer learning techniques to improve prediction accuracy across diverse datasets. This study presents a new sales order prediction framework that combines DANN-based feature extraction and various machine learning models. The DANN method generalizes the data, maintaining the data behavior’s originality. The approach addresses challenges like limited data availability and high variability in sales behavior. Using the transfer learning approach, the DANN model is trained on the training data, and this pre-trained DANN model extracts relevant features from unknown products. In contrast, Machine Learning (ML) algorithms are used to build predictive models based on it. The hyperparameter tuning of ensemble models such as Decision Tree (DT) and Random Forest (RF) is also performed. Models like the DT and RF Regressor perform better than Linear Regression and Support Vector Regressor. Notably, even without hyperparameter tuning, the Extreme Gradient Boost (XGBoost) Regressor model outperforms all the other models. This comprehensive analysis highlights the comparative benefits of various models and establishes the superiority of XGBoost in predicting sales orders effectively.

Blockchain is a groundbreaking technology widely adopted in industrial applications for improving supply chain management (SCM). The SCM and logistics communities have paid close attention to the development of blockchain technology. The primary purpose of employing a blockchain for SCM is to lower production costs while enhancing the system's security. In recent years, blockchain-related SCM research has drawn much interest, and it is fair to state that this technology is now the most promising option for delivering reliable services/goods in supply chain networks. This study uses rigorous methods to review the technical implementation aspects of SCM systems driven by Blockchain. To ensure the security of industrial applications, we primarily concentrated on developing SCM solutions with blockchain capabilities. In this study, the unique qualities of blockchain technology have been exploited to analyze the main effects of leveraging it in the SCM. Several security metrics are utilized to validate and compare the blockchain methodologies' effectiveness in SCM. The blockchain may alter the supply chain to make it more transparent and efficient by creating a useful tool for strategic planning and enhancing connections among the customers, suppliers, and accelerators. Moreover, the performance of traditional and blockchain-enabled SCM systems is compared in this study based on the parameters of efficiency, execution time, security level, and latency. [ABSTRACT FROM AUTHOR]

In recent years, applications of Blockchain technology (BCT) have been progressing at a galloping rate in miscellaneous fields, such as finance, education, travel, healthcare, and insurance. However, BCT has gained much popularity in the construction industry, especially in developed nations worldwide, as it can solve real-world problems, including poor payments, inadequate cooperation and collaboration, inappropriate data sharing among stakeholders, and poor efficiency. The current research employs a bibliometric and systematic literature review (SLR) on utilizing BCT in the construction industry. Using co-occurrence and co-citation studies, network visualization and other methodologies concerning the Web of Science (WOS) database and the research contacts’ patterns were investigated in 482 academic papers. Notable publications, conferences, significant writers, nations, organizations, and funding organizations have been acknowledged. Our research reveals that the primary study topics are BCT in the construction industry, supply chain management, smart contracts, sustainability, building information modeling (BIM), the Internet of Things (IoT) and energy efficiency. Several possible fields for further research are mentioned, including the use of BCT in: (i) circular economy, (ii) risk management, (iii) smart villages, and (iv) infrastructure construction projects.

Supply Chain Management (SCM) is a practice that has rapidly spread across industries. SCM may boost output while simultaneously satisfying customers. Despite SCM's recognition as a key factor in enhancing healthcare efficiency, widespread adoption remains in its infancy. Hospitals, a crucial element of the healthcare supply chain (HSC), have failed to fulfill the primary goals of lowering costs and providing high-quality treatment due to their inadequate knowledge of supply chain management (SCM). This research was conducted to fill in the blanks in the current HSC literature. Achieving the healthcare supply chain's goal of reducing costs will be greatly aided by the thorough literature study completed for this report. This review of healthcare supply chain management can quantify the benefits of supply chain initiatives and identify opportunities for improvement. Healthcare institutions can make informed decisions on optimizing their supply chains by understanding customer and supplier needs. This includes making strategic decisions on how to improve inventory management, streamline processes and reduce costs. The focus of this study is on the relationship between supply chain practices, the efficiency of supply chain performance, and the financial outcomes for healthcare organizations. By highlighting certain key research issues that are shared by supply chain management and healthcare management, this article contributes to the literature in both areas.

Supplier Managed Inventory (SMI) can be considered an enabler for supply chain coordination in which the supplier takes over the customer’s inventory to optimize the supply chain. However, the successful implementation of SMI is centered on the high level of trust, accurate data transfer, and efficient interaction between parties. This requires the sharing of information through supply chain stakeholders which face resistance and challenges due to the fear that this information will be revealed to its competitors and transparency of data. This paper has investigated the application of Blockchain technology and its potential for successful SMI implementation. The paper has proposed a Blockchain framework for the coordination of suppliers and customers. The framework includes a mathematical model for multiple supplier-customer order fulfillment which is embedded in the blockchain framework. The paper has demonstrated case studies to evaluate the performance of the proposed model with literature discussing the details of its blockchain framework.

The developments of the fourth industrial revolution have caused changes in all areas of society, including production. The changes in production caused by the fourth industrial revolution have also resulted in fundamental changes in the supply chain and have converted it to supply chain 4.0. Organisations must be receptive to supply chain 4.0 to maintain their competitive advantage. Therefore, this study aimed to investigate the relationships among supply chain 4.0 technologies so that, by learning and understanding these connections, industries can pave the way for the implementation of these technologies in their supply chains and use them in problem-solving. The literature review was used to identify the supply chain 4.0 technologies, and the Delphi technique was applied to extract them, including the Internet of Things (IoT), cyber-physical systems, cloud computing, big data, blockchain, artificial intelligence, Radio-frequency Identification (RFID), augmented reality, virtual reality, and simulation. The relationships of supply chain 4.0 technologies were examined using the DEMATEL technique and based on interpretive structural modelling (ISM), their deployment map was drawn. The type of technologies was determined using the MICMAC method. The MICMAC analysis found that the artificial intelligence technology is independent and, based on the findings through the DEMATEL technique, this technology is related to simulation, which belongs to the first level of the interpretive structural modelling technique, and IoT, cloud computing, big data, and blockchain technologies, which are at the second level. Based on the ISM method, RFID, virtual reality, augmented reality and simulation technologies are located at the first level; IoT, cyber-physical systems, cloud computing, big data and blockchain technologies are situated in the second level; and artificial intelligence technology belongs to the third level. According to the related literature, few studies have been conducted on the issues of supply chain 4.0 and the technologies that affect it.

This study focuses on the barriers to implementing supply chain management (SCM) 4.0 in the foundry industry and provides insights into its practical implementation in foundry units. Our study identifies barriers such as data privacy, legal issues, SCM complexity, and barriers related to implementing Industry 4.0 (I4.0)-enabled SCM in the foundry industry. It also provides insights into the role of digital infrastructure in building effective I4.0-enabled SCM. This study provides insights into how SCM 4.0 can be implemented in the foundry industry. The findings will be helpful to policymakers and foundry consultants in designing appropriate strategies for foundry units. [ABSTRACT FROM AUTHOR]

This paper is written to propose a new architecture of the smart manufacturing system from the perspective of information system theory by synthesizing the achievements and experiences gained in the process of researching and developing the smart manufacturing system suitable for the situation of the Democratic People's Republic of Korea. Verifying the structure of a smart manufacturing system suitable for the situation of the Democratic People's Republic of Korea is an essential research task that presents the goals to be achieved in the R&D and introduction of smart manufacturing systems, which are put forward by different R&D groups. Based on a global study, synthesis, and analysis of the architectural features of smart manufacturing systems required by enterprises, the authors have identified a new structure of smart manufacturing system that meets the actual conditions of the Democratic People's Republic of Korea by verifying its rationality through the practical process of studying, developing, and constructing the smart manufacturing system in the digital book editing (virtual production) and printing (physical production) of a book printing factory. The findings are that (a) a smart manufacturing system is an informative and organic combination of management information systems and the internet of things with cyber-physical system as the core; (b) a product lifecycle management system is a comprehensive information integration tool of production and management related information systems to optimize the production and management activities of enterprises and put them on a high scientific and technological basis, and its implementation is essential; and (c) research on the smart manufacturing system should be continuously conducted to meet the demands of enterprise management practice that are constantly faced with new theoretical, practical, and information technology challenges, and such research results should be realized at a high level based on strong research, development, and introduction forces in the future. The finding of the new architecture of smart manufacturing system has been made in the context of the research on the smart manufacturing system, and it will serve as the theoretical basis for the smart manufacturing system to be built in various types of enterprises in the Democratic People's Republic of Korea. [ABSTRACT FROM AUTHOR]